MapReduce
job, we consider to aggregate data with the same keys before sending them to
remote reduce tasks. Although a similar function, called combine, has been
already adopted by Hadoop, it operates immediately after a map task solely for
its generated data, failing to exploit the data aggregation opportunities among
multiple tasks on different machines. We jointly consider data partition and
aggregation for a MapReduce job with an objective that is to minimize the total
network traffic. In particular, we propose a distributed algorithm for big data
applications by decomposing the original large-scale problem into several
subproblems that can be solved in parallel. Moreover, an online algorithm is
designed to deal with the data partition and aggregation in a dynamic manner.
Finally, extensive simulation results demonstrate that our proposals can
significantly reduce network traffic cost in both offline and online cases.
1.2 INTRODUCTION
MapReduce has emerged as the most popular computing framework for big data processing due to its simple programming model and automatic management of parallel execution. MapReduce and its open source implementation Hadoop have been adopted by leading companies, such as Yahoo!, Google and Facebook, for various big data applications, such as machine learning bioinformatics and cybersecurity. MapReduce divides a computation into two main phases, namely map and reduce which in turn are carried out by several map tasks and reduce tasks, respectively. In the map phase, map tasks are launched in parallel to convert the original input splits into intermediate data in a form of key/value pairs. These key/value pairs are stored on local machine and organized into multiple data partitions, one per reduce task. In the reduce phase, each reduce task fetches its own share of data partitions from all map tasks to generate the final result.
There is a shuffle step between map and reduce phase.
In this step, the
data produced by the map phase are ordered, partitioned and transferred to the
appropriate machines executing the reduce phase. The resulting network traffic
pattern from all map tasks to all reduce tasks can cause a great volume of
network traffic, imposing a serious constraint on the efficiency of data
analytic applications. For example, with tens of thousands of machines, data
shuffling accounts for 58.6% of the cross-pod traffic and amounts to over 200
petabytes in total in the analysis of SCOPE jobs. For shuffle-heavy MapReduce
tasks, the high traffic could incur considerable performance overhead up to
30-40 % as shown in default, intermediate data are shuffled according to a hash
function in Hadoop, which would lead to large network traffic because it
ignores network topology and data size associated with each key.
We consider a toy example with two map tasks and two reduce tasks, where intermediate data of three keys K1, K2, and K3 are denoted by rectangle bars under each machine. If the hash function assigns data of K1 and K3 to reducer 1, and K2 to reducer 2, a large amount of traffic will go through the top switch. To tackle this problem incurred by the traffic-oblivious partition scheme, we take into account of both task locations and data size associated with each key in this paper. By assigning keys with larger data size to reduce tasks closer to map tasks, network traffic can be significantly reduced. In the same example above, if we assign K1 and K3 to reducer 2, and K2 to reducer 1, as shown in Fig. 1(b), the data transferred through the top switch will be significantly reduced.
To further reduce network traffic within a MapReduce job, we consider to aggregate data with the same keys before sending them to remote reduce tasks. Although a similar function, called combine, has been already adopted by Hadoop, it operates immediately after a map task solely for its generated data, failing to exploit the data aggregation opportunities among multiple tasks on different machines. As an example shown in Fig. 2(a), in the traditional scheme, two map tasks individually send data of key K1 to the reduce task. If we aggregate the data of the same keys before sending them over the top switch, as shown in Fig. 2(b), the network traffic will be reduced.
In this paper, we
jointly consider data partition and aggregation for a MapReduce job with an
objective that is to minimize the total network traffic. In particular, we
propose a distributed algorithm for big data applications by decomposing the
original large-scale problem into several subproblems that can be solved in
parallel. Moreover, an online algorithm is designed to deal with the data
partition and aggregation in a dynamic manner. Finally, extensive simulation
results demonstrate that our proposals can significantly reduce network traffic
cost in both offline and online cases.
1.3 LITRATURE SURVEY
MAPREDUCE: SIMPLIFIED DATA PROCESSING ON LARGE CLUSTERS
AUTHOR: Dean and S. Ghemawat
PUBLISH: Communications of the ACM, vol. 51, no. 1, pp. 107–113, 2008.
EXPLANATION:
MapReduce is a
programming model and an associated implementation for processing and
generating large data sets. Users specify a map function that processes a
key/value pair to generate a set of intermediate key/value pairs, and a reduce
function that merges all intermediate values associated with the same
intermediate key. Many real world tasks are expressible in this model, as shown
in the paper. Programs written in this functional style are automatically
parallelized and executed on a large cluster of commodity machines. The
run-time system takes care of the details of partitioning the input data,
scheduling the program’s execution across a set of machines, handling machine
failures, and managing the required inter-machine communication. This allows
programmers without any experience with parallel and distributed systems to
easily utilize the resources of a large distributed system. Our implementation
of MapReduce runs on a large cluster of commodity machines and is highly
scalable: a typical MapReduce computation processes many terabytes of data on
thousands of machines. Programmers find the system easy to use: hundreds of
MapReduce programs have been implemented and upwards of one thousand MapReduce
jobs are executed on Google’s clusters every day.
CLOUDBLAST: COMBINING MAPREDUCE AND VIRTUALIZATION ON DISTRIBUTED RESOURCES FOR BIOINFORMATICS APPLICATIONS
AUTHOR: A. Matsunaga, M. Tsugawa, and J. Fortes,
PUBLISH: IEEE Fourth International Conference on. IEEE, 2008, pp. 222–229.
EXPLANATION:
This paper proposes and evaluates an approach to the parallelization, deployment and management of bioinformatics applications that integrates several emerging technologies for distributed computing. The proposed approach uses the MapReduce paradigm to parallelize tools and manage their execution, machine virtualization to encapsulate their execution environments and commonly used data sets into flexibly deployable virtual machines, and network virtualization to connect resources behind firewalls/NATs while preserving the necessary performance and the communication environment. An implementation of this approach is described and used to demonstrate and evaluate the proposed approach. The implementation integrates Hadoop, Virtual Workspaces, and ViNe as the MapReduce, virtual machine and virtual network technologies, respectively, to deploy the commonly used bioinformatics tool NCBI BLAST on a WAN-based test bed consisting of clusters at two distinct locations, the University of Florida and the University of Chicago. This WAN-based implementation, called CloudBLAST, was evaluated against both non-virtualized and LAN-based implementations in order to assess the overheads of machine and network virtualization, which were shown to be insignificant. To compare the proposed approach against an MPI-based solution, CloudBLAST performance was experimentally contrasted against the publicly available mpiBLAST on the same WAN-based test bed. Both versions demonstrated performance gains as the number of available processors increased, with CloudBLAST delivering speedups of 57 against 52.4 of MPI version, when 64 processors on 2 sites were used. The results encourage the use of the proposed approach for the execution of large-scale bioinformatics applications on emerging distributed environments that provide access to computing resources as a service.
MAP TASK SCHEDULING IN MAPREDUCE WITH DATA LOCALITY: THROUGHPUT AND HEAVY-TRAFFIC OPTIMALITY
AUTHOR: W. Wang, K. Zhu, L. Ying, J. Tan, and L. Zhang
PUBLISH: INFOCOM, 2013 Proceedings IEEE. IEEE, 2013, pp. 1609–1617.
EXPLANATION:
Scheduling map tasks to improve data locality is crucial to the performance of MapReduce. Many works have been devoted to increasing data locality for better efficiency. However, to the best of our knowledge, fundamental limits of MapReduce computing clusters with data locality, including the capacity region and theoretical bounds on the delay performance, have not been studied. In this paper, we address these problems from a stochastic network perspective. Our focus is to strike the right balance between data-locality and load-balancing to simultaneously maximize throughput and minimize delay.
We
present a new queueing architecture and propose a map task scheduling algorithm
constituted by the Join the Shortest Queue policy together with the MaxWeight
policy. We identify an outer bound on the capacity region, and then prove that
the proposed algorithm stabilizes any arrival rate vector strictly within this
outer bound. It shows that the algorithm is throughput optimal and the outer
bound coincides with the actual capacity region. Further, we study the number
of backlogged tasks under the proposed algorithm, which is directly related to
the delay performance based on Little’s law. We prove that the proposed
algorithm is heavy-traffic optimal, i.e., it asymptotically minimizes the
number of backlogged tasks as the arrival rate vector approaches the boundary
of the capacity region. Therefore, the proposed algorithm is also delay optimal
in the heavy-traffic regime.
CHAPTER 2
2.0 SYSTEM ANALYSIS
2.1 EXISTING SYSTEM:
Existing problem of optimizing network usage in MapReduce scheduling in the reason that we are interested in network usage is twofold. Firstly, network utilization is a quantity of independent interest, as it is directly related to the throughput of the system. Note that the total amount of data processed in unit time is simply (CPU utilization)·(CPU capacity)+ (network utilization)·(network capacity). CPU utilization will always be 1 as long as there are enough jobs in the map queue, but network utilization can be very sensitive to scheduling network utilization has been identified as a key component in optimization of MapReduce systems in several previous works.
Network usage could lead us to algorithms with smaller mean response time. We find the main motivation for this direction of our work in the results of the aforementioned overlap between map and shuffle phases, are shown to yield significantly better mean response time than Hadoop’s fair scheduler. However, we observed that neither of these two algorithms explicitly attempted to optimize network usage, which suggested room for improvement. MapReduce has become one of the most popular frameworks for large-scale distributed computing, there exists a huge body of work regarding performance optimization of MapReduce.
For instance, researchers have tried to optimize MapReduce systems by efficiently detecting and eliminating the so-called “stragglers” providing better locality of data preventing starvation caused by large jobs analyzing the problem from a purely theoretical viewpoint of shuffle workload available at any given time is closely related to the output rate of the map phase, due to the inherent dependency between the map and shuffle phases. In particular, when the job that is being processed is ‘map-heavy,’ the available workload of the same job in the shuffle phase is upper-bounded by the output rate of the map phase. Therefore, poor scheduling of map tasks can have adverse effects on the throughput of the shuffle phase, causing the network to be idle and the efficiency of the entire system to decrease.
2.1.1 DISADVANTAGES:
Existing model, called the overlapping tandem queue model, is a job-level model for MapReduce where the map and shuffle phases of the MapReduce framework are modeled as two queues that are put in tandem. Since it is a job-level model, each job is represented by only the map size and the shuffle size simplification is justified by the introduction of two main assumptions. The first assumption states that each job consists of a large number of small-sized tasks, which allows us to represent the progress of each phase by real numbers.
The job-level model offers two big disadvantages over the more complicated task-level models.
Firstly, it gives rise to algorithms that are much simpler than those of task-level models, which enhances chances of being deployed in an actual system.
Secondly,
the number of jobs in a system is often smaller than the number of tasks by
several orders of magnitude, making the problem computationally much less
strenuous note that there are still some questions to be studied regarding the
general applicability of the additional assumptions of the job-level model,
which are interesting research questions in their own light
2.2 PROPOSED SYSTEM:
In this paper, we jointly consider data partition and aggregation for a MapReduce job with an objective that is to minimize the total network traffic. In particular, we propose a distributed algorithm for big data applications by decomposing the original large-scale problem into several subproblems that can be solved in parallel. Moreover, an online algorithm is designed to deal with the data partition and aggregation in a dynamic manner. Finally, extensive simulation results demonstrate that our proposals can significantly reduce network traffic cost in both offline and online cases.
MapReduce resource allocation system, to enhance the performance of MapReduce jobs in the cloud by locating intermediate data to the local machines or close-by physical machines in this locality-awareness reduces network traffic in the shuffle phase generated in the cloud data center. However, little work has studied to optimize network performance of the shuffle process that generates large amounts of data traffic in MapReduce jobs. A critical factor to the network performance in the shuffle phase is the intermediate data partition. The default scheme adopted by Hadoop is hash-based partition that would yield unbalanced loads among reduce tasks due to its unawareness of the data size associated with each key.
We have
developed a fairness-aware key partition approach that keeps track of the
distribution of intermediate keys’ frequencies, and guarantees a fair
distribution among reduce tasks. have introduced a combiner function that
reduces the amount of data to be shuffled and merged to reduce tasks an
in-mapper combining scheme by exploiting the fact that mappers can preserve
state across the processing of multiple input key/value pairs and defer emission
of intermediate data until all input records have been processed. Both
proposals are constrained to a single map task, ignoring the data aggregation
opportunities from multiple map tasks a MapReduce-like system to decrease the
traffic by pushing aggregation from the edge into the network.
2.2.1 ADVANTAGES:
2.3.1 HARDWARE REQUIREMENT:
CHAPTER 3
3.0 SYSTEM DESIGN:
Data Flow Diagram / Use Case Diagram / Flow Diagram:
External sources or destinations, which may be people or organizations or other entities
Here the data referenced by a process is stored and retrieved.
People, procedures or devices that produce data’s in the physical component is not identified.
Data moves in a
specific direction from an origin to a destination. The data flow is a “packet”
of data.
MODELING RULES:
There are several common modeling rules when creating DFDs:
3.1
ARCHITECTURE DIAGRAM
3.2 DATAFLOW DIAGRAM
| SERVER |
| Access Layer |
| Cross Layer |
| Use Hash Partition |
| Traffic Aware Partition |
| Send data through head node |
| Send data through head node |
| Mapper |
| RECEIVER |
| Aggregation Layer |
| Map Reducer |
| OHRA |
| OHNA |
UML DIAGRAMS:
3.2 USE CASE DIAGRAM:
| Source |
| Destination |
| Establish connection |
| Send the data |
| Data send into destination |
| Data Aggregation Layer |
| Receive data |
| Neighbor Nodes |
| View data |
| Base station |
| Form the cluster |
3.3 CLASS DIAGRAM:
| Source |
| Base station |
| System Address |
| Data Send () |
| Data send |
| Data info |
| Destination address |
| Data Send |
| Transmitting () |
| Destination |
| System Address () |
| Maintain Details |
| Verify () |
| Receive data () |
| View data () |
| Connection () |
| Move Nodes |
| Node info |
| Data length |
| Hop routing () |
3.4 SEQUENCE DIAGRAM:
| Connection established |
Send data
Data Aggregation Layer
Form routing
Routing Finished
Traffic Aware Partition
Connection terminate
Source
Base station
Destination
Establish communication
Connection established
Receiving Ack
Data received
Map Reducer
3.5 ACTIVITY DIAGRAM:
| Source |
| Destination |
| False |
| Receive data |
| View data |
| True |
| False |
| Connection establish |
| Send data |
| Aggregation Node |
| Receive Ack |
| True |
| Using Mapper |
| Data transfer |
| Map Reducer |
| Base station |
CHAPTER 4
4.0 IMPLEMENTATION:
ONLINE EXTENSION OF HRA AND HNA
In this section, we conduct extensive simulations to evaluate the performance of our proposed distributed algorithm DA. We compare DA with HNA, which is the default method in Hadoop. To our best knowledge, we are the first to propose the aggregator placement algorithm, and compared with the HRA that focuses on a random aggregator placement. All simulation results are averaged over 30 random instances.
• HNA: Hash-based partition with No Aggregation. It exploits the traditional hash partitioning for the intermediate data, which are transferred to reducers without going through aggregators. It is the default method in Hadoop.
• HRA: Hash-based partition with Random Aggregation. It adds a random aggregator placement algorithm based on the traditional Hadoop. Through randomly placing aggregators in the shuffle phase, it aims to reducing the network traffic cost in the comparison of traditional method in Hadoop.
Our proposed
distributed algorithm and the optimal solution obtained by solving the MILP
formulation. Due to the high computational complexity of the MILP formulation,
we consider small-scale problem instances with 10 keys in this set of
simulations. Each key associated with random data size within [1-50]. There are
20 mappers, and 2 reducers on a cluster of 20 machines. The parameter α is set
to 0.5. The distance between any two machines is randomly chosen within [1-60].
As shown in Fig. 7, the performance of our distributed algorithm is very close
to the optimal solution. Although network traffic cost increases as the number
of keys grows for all algorithms, the performance enhancement of our proposed
algorithms to the other two schemes becomes larger. When the number of keys is
set to 10, the default algorithm HNA has a cost of 5.0 × 104 while optimal solution
is only 2.7×104 , with 46% traffic reduction.
4.1 ALGORITHM
DISTRIBUTED ALGORITHM
The problem above
can be solved by highly efficient approximation algorithms, e.g.,
branch-and-bound, and fast off-the-shelf solvers, e.g., CPLEX, for
moderate-sized input. An additional challenge arises in dealing with the
MapReduce job for big data. In such a job, there are hundreds or even thousands
of keys, each of which is associated with a set of variables (e.g., x p ij and
y p k ) and constraints in our formulation, leading to a large-scale
optimization problem that is hardly handled by existing algorithms and solvers
in practice.
ONLINE ALGORITHM
We take the data size m p i and data aggregation ratio αj as input of our algorithms. In order to get their values, we need to wait all mappers to finish before starting reduce tasks, or conduct estimation via profiling on a small set of data. In practice, map and reduce tasks may partially overlap in execution to increase system throughput, and it is difficult to estimate system parameters at a high accuracy for big data applications. These motivate us to design an online algorithm to dynamically adjust data partition and aggregation during the execution of map and reduce tasks.
4.2 MODULES:
SERVER CLIENTS:
DITRIBUTED DATA:
SHEDULING TASK:
NETWORK TRAFFIC TRACES:
MAPREDUCE TASK:
4.3 MODULE DESCRIPTION:
SERVER CLIENTS:
Client-server computing or networking is a distributed application architecture that partitions tasks or workloads between service providers (servers) and service requesters, called clients. Often clients and servers operate over a computer network on separate hardware. A server machine is a high-performance host that is running one or more server programs which share its resources with clients. A client also shares any of its resources; Clients therefore initiate communication sessions with servers which await (listen to) incoming requests.
DITRIBUTED DATA:
We develop a distributed algorithm to solve
the problem on multiple machines in a parallel manner. Our basic idea is to
decompose the original large-scale problem into several distributively solvable
subproblems that are coordinated by a high-level master problem. We jointly
consider data partition and aggregation for a MapReduce job with an objective
that is to minimize the total network traffic. In particular, we propose a
distributed algorithm for big data applications by decomposing the original
large-scale problem into several subproblems that can be solved in parallel. Moreover,
an online algorithm is designed to deal with the data partition and aggregation
in a dynamic manner. Finally, extensive simulation results demonstrate that our
proposals can significantly reduce network traffic cost in both offline and
online cases.
SHEDULING TASK:
MapReduce divides a computation into two main phases, namely map and reduce which in turn are carried out by several map tasks and reduce tasks, respectively. In the map phase, map tasks are launched in parallel to convert the original input splits into intermediate data in a form of key/value pairs. These key/value pairs are stored on local machine and organized into multiple data partitions, one per reduce task. In the reduce phase, each reduce task fetches its own share of data partitions from all map tasks to generate the final result. There is a shuffle step between map and reduce phase. In this step, the data produced by the map phase are ordered, partitioned and transferred to the appropriate machines executing the reduce phase. The resulting network traffic pattern from all map tasks to all reduce tasks can cause a great volume of network traffic, imposing a serious constraint on the efficiency of data analytic applications.
NETWORK TRAFFIC TRACES:
Network traffic within a MapReduce job,
we consider to aggregate data with the same keys before sending them to remote reduce
tasks. Although a similar function, called combiner has been already adopted by
Hadoop, it operates immediately after a map task solely for its generated data,
failing to exploit the data aggregation opportunities among multiple tasks on different
machines. As an example shown in Fig. 2(a), in the traditional scheme, two map
tasks individually send data of key K1 to the reduce task. If we aggregate the data
of the same keys before sending them over the top switch, as shown in Fig.
2(b), the network traffic will be reduced. We tested the real network traffic
cost in Hadoop using the real data source from latest dumps files in Wikimedia (http://dumps.wikimedia.org/enwiki/latest/).
In the meantime, we executed our distributed algorithm using the same data
source for comparison. Since our distributed algorithm is based on a known
aggregation ratio _, we have done some experiments to evaluate it in Hadoop
environment.
MAPREDUCE TASK:
We focus on MapReduce performance improvement by optimizing its data transmission optimizing network usage can lead to better system performance and found that high network utilization and low network congestion should be achieved simultaneously for a job with good performance. MapReduce resource allocation system, to enhance the performance of MapReduce jobs in the cloud by locating intermediate data to the local machines or close-by physical machines locality-awareness reduces network traffic in the shuffle phase generated in the cloud data center. However, little work has studied to optimize network performance of the shuffle process that generates large amounts of data traffic in MapReduce jobs. A critical factor to the network performance in the shuffle phase is the intermediate data partition. The default scheme adopted by Hadoop is hash-based partition that would yield unbalanced loads among reduce tasks due to its unawareness of the data size associated with each key.
To overcome this shortcoming, we have developed
a fairness-aware key partition approach that keeps track of the distribution of
intermediate keys’ frequencies, and guarantees a fair distribution among reduce
tasks. In addition to data partition, many efforts have been made on local
aggregation, in-mapper combining and in-network aggregation to reduce network
traffic within MapReduce jobs. have introduced a combiner function that reduces
the amount of data to be shuffled and merged to reduce tasks an in-mapper
combining scheme by exploiting the fact that mappers can preserve state across the
processing of multiple input key/value pairs and defer emission of intermediate
data until all input records have been processed. Both proposals are
constrained to a single map task, ignoring the data aggregation opportunities
from multiple map tasks have proposed a MapReduce-like system to decrease the traffic
by pushing aggregation from the edge into the network.
CHAPTER 5
5.0 SYSTEM STUDY:
5.1 FEASIBILITY STUDY:
The feasibility of the project is analyzed in this phase and business proposal is put forth with a very general plan for the project and some cost estimates. During system analysis the feasibility study of the proposed system is to be carried out. This is to ensure that the proposed system is not a burden to the company. For feasibility analysis, some understanding of the major requirements for the system is essential.
Three key considerations involved in the feasibility analysis are
5.1.1 ECONOMICAL FEASIBILITY:
This study is carried out to check the economic impact that the system will have on the organization. The amount of fund that the company can pour into the research and development of the system is limited. The expenditures must be justified. Thus the developed system as well within the budget and this was achieved because most of the technologies used are freely available. Only the customized products had to be purchased.
This study is carried out to check the technical feasibility, that is, the technical requirements of the system. Any system developed must not have a high demand on the available technical resources. This will lead to high demands on the available technical resources. This will lead to high demands being placed on the client. The developed system must have a modest requirement, as only minimal or null changes are required for implementing this system.
5.1.3 SOCIAL FEASIBILITY:
The aspect of study is to check the level of acceptance of the system by the user. This includes the process of training the user to use the system efficiently. The user must not feel threatened by the system, instead must accept it as a necessity. The level of acceptance by the users solely depends on the methods that are employed to educate the user about the system and to make him familiar with it. His level of confidence must be raised so that he is also able to make some constructive criticism, which is welcomed, as he is the final user of the system.
5.2 SYSTEM TESTING:
Testing is a process of checking whether the developed system is working according to the original objectives and requirements. It is a set of activities that can be planned in advance and conducted systematically. Testing is vital to the success of the system. System testing makes a logical assumption that if all the parts of the system are correct, the global will be successfully achieved. In adequate testing if not testing leads to errors that may not appear even many months.
This creates two problems, the time lag between the cause and the appearance of the problem and the effect of the system errors on the files and records within the system. A small system error can conceivably explode into a much larger Problem. Effective testing early in the purpose translates directly into long term cost savings from a reduced number of errors. Another reason for system testing is its utility, as a user-oriented vehicle before implementation. The best programs are worthless if it produces the correct outputs.
5.2.1 UNIT TESTING:
| Description | Expected result |
| Test for application window properties. | All the properties of the windows are to be properly aligned and displayed. |
| Test for mouse operations. | All the mouse operations like click, drag, etc. must perform the necessary operations without any exceptions. |
A program
represents the logical elements of a system. For a program to run satisfactorily,
it must compile and test data correctly and tie in properly with other
programs. Achieving an error free program is the responsibility of the
programmer. Program testing checks
for two types
of errors: syntax
and logical. Syntax error is a
program statement that violates one or more rules of the language in which it
is written. An improperly defined field dimension or omitted keywords are
common syntax errors. These errors are shown through error message generated by
the computer. For Logic errors the programmer must examine the output
carefully.
5.1.2 FUNCTIONAL TESTING:
Functional testing of an application is used to prove the application delivers correct results, using enough inputs to give an adequate level of confidence that will work correctly for all sets of inputs. The functional testing will need to prove that the application works for each client type and that personalization function work correctly.When a program is tested, the actual output is compared with the expected output. When there is a discrepancy the sequence of instructions must be traced to determine the problem. The process is facilitated by breaking the program into self-contained portions, each of which can be checked at certain key points. The idea is to compare program values against desk-calculated values to isolate the problems.
| Description | Expected result |
| Test for all modules. | All peers should communicate in the group. |
| Test for various peer in a distributed network framework as it display all users available in the group. | The result after execution should give the accurate result. |
5.1. 3 NON-FUNCTIONAL TESTING:
The Non Functional software testing encompasses a rich spectrum of testing strategies, describing the expected results for every test case. It uses symbolic analysis techniques. This testing used to check that an application will work in the operational environment. Non-functional testing includes:
5.1.4 LOAD TESTING:
An important tool for implementing system tests is a Load generator. A Load generator is essential for testing quality requirements such as performance and stress. A load can be a real load, that is, the system can be put under test to real usage by having actual telephone users connected to it. They will generate test input data for system test.
| Description | Expected result |
| It is necessary to ascertain that the application behaves correctly under loads when ‘Server busy’ response is received. | Should designate another active node as a Server. |
5.1.5 PERFORMANCE TESTING:
Performance tests are utilized in order to determine the widely defined performance of the software system such as execution time associated with various parts of the code, response time and device utilization. The intent of this testing is to identify weak points of the software system and quantify its shortcomings.
| Description | Expected result |
| This is required to assure that an application perforce adequately, having the capability to handle many peers, delivering its results in expected time and using an acceptable level of resource and it is an aspect of operational management. | Should handle large input values, and produce accurate result in a expected time. |
5.1.6 RELIABILITY TESTING:
The software reliability is the ability of a system or component to perform its required functions under stated conditions for a specified period of time and it is being ensured in this testing. Reliability can be expressed as the ability of the software to reveal defects under testing conditions, according to the specified requirements. It the portability that a software system will operate without failure under given conditions for a given time interval and it focuses on the behavior of the software element. It forms a part of the software quality control team.
| Description | Expected result |
| This is to check that the server is rugged and reliable and can handle the failure of any of the components involved in provide the application. | In case of failure of the server an alternate server should take over the job. |
5.1.7 SECURITY TESTING:
Security testing evaluates system characteristics that relate to the availability, integrity and confidentiality of the system data and services. Users/Clients should be encouraged to make sure their security needs are very clearly known at requirements time, so that the security issues can be addressed by the designers and testers.
| Description | Expected result |
| Checking that the user identification is authenticated. | In case failure it should not be connected in the framework. |
| Check whether group keys in a tree are shared by all peers. | The peers should know group key in the same group. |
5.1.8 WHITE BOX TESTING:
White box testing, sometimes called glass-box testing is a test case design method that uses the control structure of the procedural design to derive test cases. Using white box testing method, the software engineer can derive test cases. The White box testing focuses on the inner structure of the software structure to be tested.
| Description | Expected result |
| Exercise all logical decisions on their true and false sides. | All the logical decisions must be valid. |
| Execute all loops at their boundaries and within their operational bounds. | All the loops must be finite. |
| Exercise internal data structures to ensure their validity. | All the data structures must be valid. |
5.1.9 BLACK BOX TESTING:
Black box testing, also called behavioral testing, focuses on the functional requirements of the software. That is, black testing enables the software engineer to derive sets of input conditions that will fully exercise all functional requirements for a program. Black box testing is not alternative to white box techniques. Rather it is a complementary approach that is likely to uncover a different class of errors than white box methods. Black box testing attempts to find errors which focuses on inputs, outputs, and principle function of a software module. The starting point of the black box testing is either a specification or code. The contents of the box are hidden and the stimulated software should produce the desired results.
| Description | Expected result |
| To check for incorrect or missing functions. | All the functions must be valid. |
| To check for interface errors. | The entire interface must function normally. |
| To check for errors in a data structures or external data base access. | The database updation and retrieval must be done. |
| To check for initialization and termination errors. | All the functions and data structures must be initialized properly and terminated normally. |
All
the above system testing strategies are carried out in as the development,
documentation and institutionalization of the proposed goals and related
policies is essential.
CHAPTER 6
6.0 SOFTWARE DESCRIPTION:
Java technology is both a programming language and a platform.
With most programming languages, you either compile or interpret a program so that you can run it on your computer. The Java programming language is unusual in that a program is both compiled and interpreted. With the compiler, first you translate a program into an intermediate language called Java byte codes —the platform-independent codes interpreted by the interpreter on the Java platform. The interpreter parses and runs each Java byte code instruction on the computer. Compilation happens just once; interpretation occurs each time the program is executed. The following figure illustrates how this works.
You can think of Java byte codes as the machine code instructions for the Java Virtual Machine (Java VM). Every Java interpreter, whether it’s a development tool or a Web browser that can run applets, is an implementation of the Java VM. Java byte codes help make “write once, run anywhere” possible. You can compile your program into byte codes on any platform that has a Java compiler. The byte codes can then be run on any implementation of the Java VM. That means that as long as a computer has a Java VM, the same program written in the Java programming language can run on Windows 2000, a Solaris workstation, or on an iMac.
A platform is the hardware or software environment in which a program runs. We’ve already mentioned some of the most popular platforms like Windows 2000, Linux, Solaris, and MacOS. Most platforms can be described as a combination of the operating system and hardware. The Java platform differs from most other platforms in that it’s a software-only platform that runs on top of other hardware-based platforms.
The Java platform has two components:
You’ve already been introduced to the Java VM. It’s the base for the Java platform and is ported onto various hardware-based platforms.
The Java API is a large collection of ready-made software components that provide many useful capabilities, such as graphical user interface (GUI) widgets. The Java API is grouped into libraries of related classes and interfaces; these libraries are known as packages. The next section, What Can Java Technology Do? Highlights what functionality some of the packages in the Java API provide.
The following figure depicts a program that’s running on the Java platform. As the figure shows, the Java API and the virtual machine insulate the program from the hardware.
Native code is code that after you compile it, the compiled code runs on a specific hardware platform. As a platform-independent environment, the Java platform can be a bit slower than native code. However, smart compilers, well-tuned interpreters, and just-in-time byte code compilers can bring performance close to that of native code without threatening portability.
The most common types of programs written in the Java programming language are applets and applications. If you’ve surfed the Web, you’re probably already familiar with applets. An applet is a program that adheres to certain conventions that allow it to run within a Java-enabled browser.
However, the Java programming language is not just for writing cute, entertaining applets for the Web. The general-purpose, high-level Java programming language is also a powerful software platform. Using the generous API, you can write many types of programs.
An application is a standalone program that runs directly on the Java platform. A special kind of application known as a server serves and supports clients on a network. Examples of servers are Web servers, proxy servers, mail servers, and print servers. Another specialized program is a servlet.
A servlet can almost be thought of as an applet that runs on the server side. Java Servlets are a popular choice for building interactive web applications, replacing the use of CGI scripts. Servlets are similar to applets in that they are runtime extensions of applications. Instead of working in browsers, though, servlets run within Java Web servers, configuring or tailoring the server.
How does the API support all these kinds of programs? It does so with packages of software components that provides a wide range of functionality. Every full implementation of the Java platform gives you the following features:
The Java platform also has APIs for 2D and 3D graphics, accessibility, servers, collaboration, telephony, speech, animation, and more. The following figure depicts what is included in the Java 2 SDK.
We can’t promise you fame, fortune, or even a job if you learn the Java programming language. Still, it is likely to make your programs better and requires less effort than other languages. We believe that Java technology will help you do the following:
Microsoft Open Database Connectivity (ODBC) is a standard programming interface for application developers and database systems providers. Before ODBC became a de facto standard for Windows programs to interface with database systems, programmers had to use proprietary languages for each database they wanted to connect to. Now, ODBC has made the choice of the database system almost irrelevant from a coding perspective, which is as it should be. Application developers have much more important things to worry about than the syntax that is needed to port their program from one database to another when business needs suddenly change.
Through the ODBC Administrator in Control Panel, you can specify the particular database that is associated with a data source that an ODBC application program is written to use. Think of an ODBC data source as a door with a name on it. Each door will lead you to a particular database. For example, the data source named Sales Figures might be a SQL Server database, whereas the Accounts Payable data source could refer to an Access database. The physical database referred to by a data source can reside anywhere on the LAN.
The ODBC system files are not installed on your system by Windows 95. Rather, they are installed when you setup a separate database application, such as SQL Server Client or Visual Basic 4.0. When the ODBC icon is installed in Control Panel, it uses a file called ODBCINST.DLL. It is also possible to administer your ODBC data sources through a stand-alone program called ODBCADM.EXE. There is a 16-bit and a 32-bit version of this program and each maintains a separate list of ODBC data sources.
From a programming perspective, the beauty of ODBC is that the application can be written to use the same set of function calls to interface with any data source, regardless of the database vendor. The source code of the application doesn’t change whether it talks to Oracle or SQL Server. We only mention these two as an example. There are ODBC drivers available for several dozen popular database systems. Even Excel spreadsheets and plain text files can be turned into data sources. The operating system uses the Registry information written by ODBC Administrator to determine which low-level ODBC drivers are needed to talk to the data source (such as the interface to Oracle or SQL Server). The loading of the ODBC drivers is transparent to the ODBC application program. In a client/server environment, the ODBC API even handles many of the network issues for the application programmer.
The advantages
of this scheme are so numerous that you are probably thinking there must be
some catch. The only disadvantage of ODBC is that it isn’t as efficient as
talking directly to the native database interface. ODBC has had many detractors
make the charge that it is too slow. Microsoft has always claimed that the
critical factor in performance is the quality of the driver software that is
used. In our humble opinion, this is true. The availability of good ODBC
drivers has improved a great deal recently. And anyway, the criticism about
performance is somewhat analogous to those who said that compilers would never
match the speed of pure assembly language. Maybe not, but the compiler (or
ODBC) gives you the opportunity to write cleaner programs, which means you
finish sooner. Meanwhile, computers get faster every year.
6.6 JDBC:
In an effort to set an independent database standard API for Java; Sun Microsystems developed Java Database Connectivity, or JDBC. JDBC offers a generic SQL database access mechanism that provides a consistent interface to a variety of RDBMSs. This consistent interface is achieved through the use of “plug-in” database connectivity modules, or drivers. If a database vendor wishes to have JDBC support, he or she must provide the driver for each platform that the database and Java run on.
To gain a wider acceptance of JDBC, Sun based JDBC’s framework on ODBC. As you discovered earlier in this chapter, ODBC has widespread support on a variety of platforms. Basing JDBC on ODBC will allow vendors to bring JDBC drivers to market much faster than developing a completely new connectivity solution.
JDBC was announced in March of 1996. It was released for a 90 day public review that ended June 8, 1996. Because of user input, the final JDBC v1.0 specification was released soon after.
The remainder of this section will cover enough information about JDBC for you to know what it is about and how to use it effectively. This is by no means a complete overview of JDBC. That would fill an entire book.
Few software packages are designed without goals in mind. JDBC is one that, because of its many goals, drove the development of the API. These goals, in conjunction with early reviewer feedback, have finalized the JDBC class library into a solid framework for building database applications in Java.
The goals that were set for JDBC are important. They will give you some insight as to why certain classes and functionalities behave the way they do. The eight design goals for JDBC are as follows:
SQL Level API
The designers felt that their main goal was to define a SQL interface for Java. Although not the lowest database interface level possible, it is at a low enough level for higher-level tools and APIs to be created. Conversely, it is at a high enough level for application programmers to use it confidently. Attaining this goal allows for future tool vendors to “generate” JDBC code and to hide many of JDBC’s complexities from the end user.
SQL Conformance
SQL syntax varies as you move from database vendor to database vendor. In an effort to support a wide variety of vendors, JDBC will allow any query statement to be passed through it to the underlying database driver. This allows the connectivity module to handle non-standard functionality in a manner that is suitable for its users.
JDBC must be implemental on top of common database interfaces
The JDBC SQL API must “sit” on top of other common SQL level APIs. This goal allows JDBC to use existing ODBC level drivers by the use of a software interface. This interface would translate JDBC calls to ODBC and vice versa.
Because of Java’s acceptance in the user community thus far, the designers feel that they should not stray from the current design of the core Java system.
This goal probably appears in all software design goal listings. JDBC is no exception. Sun felt that the design of JDBC should be very simple, allowing for only one method of completing a task per mechanism. Allowing duplicate functionality only serves to confuse the users of the API.
Strong typing allows for more error checking to be done at compile time; also, less error appear at runtime.
Because more often than not, the usual SQL calls
used by the programmer are simple SELECT’s,
INSERT’s,
DELETE’s
and UPDATE’s,
these queries should be simple to perform with JDBC. However, more complex SQL
statements should also be possible.
Finally we decided to precede the implementation using Java Networking.
And for dynamically updating the cache table we go for MS Access database.
Java ha two things: a programming language and a platform.
Java is a high-level programming language that is all of the following
Simple Architecture-neutral
Object-oriented Portable
Distributed High-performance
Interpreted Multithreaded
Robust Dynamic Secure
Java is also unusual in that each Java program is both compiled and interpreted. With a compile you translate a Java program into an intermediate language called Java byte codes the platform-independent code instruction is passed and run on the computer.
Compilation happens just once; interpretation occurs each time the program is executed. The figure illustrates how this works.
| Java Program |
| Compilers |
| Interpreter |
| My Program |
The TCP/IP stack is shorter than the OSI one:
TCP is a connection-oriented protocol; UDP (User Datagram Protocol) is a connectionless protocol.
The IP layer provides a connectionless and unreliable delivery system. It considers each datagram independently of the others. Any association between datagram must be supplied by the higher layers. The IP layer supplies a checksum that includes its own header. The header includes the source and destination addresses. The IP layer handles routing through an Internet. It is also responsible for breaking up large datagram into smaller ones for transmission and reassembling them at the other end.
UDP is also connectionless and unreliable. What it adds to IP is a checksum for the contents of the datagram and port numbers. These are used to give a client/server model – see later.
TCP supplies logic to give a reliable connection-oriented protocol above IP. It provides a virtual circuit that two processes can use to communicate.
In order to use a service, you must be able to find it. The Internet uses an address scheme for machines so that they can be located. The address is a 32 bit integer which gives the IP address.
Class A uses 8 bits for the network address with 24 bits left over for other addressing. Class B uses 16 bit network addressing. Class C uses 24 bit network addressing and class D uses all 32.
Internally, the UNIX network is divided into sub networks. Building 11 is currently on one sub network and uses 10-bit addressing, allowing 1024 different hosts.
8 bits are finally used for host addresses within our subnet. This places a limit of 256 machines that can be on the subnet.
The 32 bit address is usually written as 4 integers separated by dots.
A service exists on a host, and is identified by its port. This is a 16 bit number. To send a message to a server, you send it to the port for that service of the host that it is running on. This is not location transparency! Certain of these ports are “well known”.
A socket is a data structure maintained by the system
to handle network connections. A socket is created using the call socket. It returns an integer that is like a file descriptor.
In fact, under Windows, this handle can be used with Read File and Write File
functions.
#include <sys/types.h>
#include <sys/socket.h>
int socket(int family, int type, int protocol);
Here “family” will be AF_INET for IP communications, protocol will be zero, and type will depend on whether TCP or UDP is used. Two
processes wishing to communicate over a network create a socket each. These are
similar to two ends of a pipe – but the actual pipe does not yet exist.
6.8 JFREE CHART:
JFreeChart is a free 100% Java chart library that makes it easy for developers to display professional quality charts in their applications. JFreeChart’s extensive feature set includes:
A consistent and well-documented API, supporting a wide range of chart types;
A flexible design that is easy to extend, and targets both server-side and client-side applications;
Support for many output types, including Swing components, image files (including PNG and JPEG), and vector graphics file formats (including PDF, EPS and SVG);
JFreeChart is “open source” or, more specifically, free software. It is distributed under the terms of the GNU Lesser General Public Licence (LGPL), which permits use in proprietary applications.
Charts showing values that relate to geographical areas. Some examples include: (a) population density in each state of the United States, (b) income per capita for each country in Europe, (c) life expectancy in each country of the world. The tasks in this project include: Sourcing freely redistributable vector outlines for the countries of the world, states/provinces in particular countries (USA in particular, but also other areas);
Creating an appropriate dataset interface (plus
default implementation), a rendered, and integrating this with the existing
XYPlot class in JFreeChart; Testing, documenting, testing some more,
documenting some more.
Implement a new (to JFreeChart) feature for interactive time series charts — to display a separate control that shows a small version of ALL the time series data, with a sliding “view” rectangle that allows you to select the subset of the time series data to display in the main chart.
There is currently a lot of interest in dashboard displays. Create a flexible dashboard mechanism that supports a subset of JFreeChart chart types (dials, pies, thermometers, bars, and lines/time series) that can be delivered easily via both Java Web Start and an applet.
The property editor mechanism in JFreeChart only
handles a small subset of the properties that can be set for charts. Extend (or
reimplement) this mechanism to provide greater end-user control over the
appearance of the charts.
CHAPTER 7
7.0 APPENDIX
7.1 SAMPLE SCREEN SHOTS:
7.2
SAMPLE SOURCE CODE:
CHAPTER 8
8.1 CONCLUSION
In this
paper, we study the joint optimization of intermediate data partition and
aggregation in MapReduce to minimize network traffic cost for big data
applications. We propose a three-layer model for this problem and formulate it
as a mixed-integer nonlinear problem, which is then transferred into a linear
form that can be solved by mathematical tools. To deal with the large-scale
formulation due to big data, we design a distributed algorithm to solve the
problem on multiple machines. Furthermore, we extend our algorithm to handle
the MapReduce job in an online manner when some system parameters are not
given. Finally, we conduct extensive simulations to evaluate our proposed
algorithm under both offline cases and online cases. The simulation results
demonstrate that our proposals can effectively reduce network traffic cost
under various network settings.
CHAPTER 9